Gauging the impact of meta-analysis on ecology

Abstract

Meta-analyses are an increasingly used set of statistical tools that allow for data from multiple studies to be drawn together allowing broader, more generalizable conclusions. The extent to which the increase in the number of meta-analyses in ecology, relative to other types of papers, has influenced how questions are asked and the current state of knowledge has not been assessed before. Here, we gauge the impact of meta-analyses in ecology quantitatively and qualitatively. For the quantitative assessment, we conducted an analysis of 240 published meta-analyses to examine trends in ecological meta-analyses. Our examination shows that publication rates of meta-analyses in ecology have increased through time, and that more recent meta-analyses have been more comprehensive, including more studies and a greater temporal range of studies. Meta-analyses in ecology are the result of larger collaborations with meta-analyses being authored by larger teams than other studies, and those funded by collaborative centers have even larger collaborations. These larger collaborations result in a larger scope and scale of the analyses—by using more papers, datasets, species and years of data. Qualitatively, we discuss three examples: the strength of competition, the nature of how biodiversity affects ecosystem function, and the response of species to global climate change, where meta-analyses supplied the critical evaluation of accepted ecological explanations. As scientific criticism and controversy mount, the true power of meta-analyses is to serve as the capstone evidence supporting the validity of an explanation and to possibly herald the shift to other potential explanations.

Notes

Acknowledgments

We wish to thank the editors of this special issue for their invitation and to two anonymous reviewers for helping to greatly improve this paper. Funding for this work was generously provided by the National Center for Ecological Analysis and Synthesis, a Center funded by NSF (Grant #DEB-0553768), the University of California, Santa Barbara, and the State of California and an NSERC grant (386151) to MWC.